Background: Bluetongue virus (BTV) is an arbovirus that causes lots of economic losses worldwide. The most common method of transmission is by vector Culicoides midges. Due to this close relationship between the BTV infection and the vectors, many climate-related risk factors play a role in the occurrence of the disease. The predictive ability of Logistic Regression (LR), Support Vector Machines (SVM), Decision Tree (DT), Random Forest (RF), XGBoost and Artificial Neural Networks (ANN) algorithms in predicting the BTV infection occurrence was assessed. Evaluated predictive risk factors included 19 standard bioclimatic variables, meteorological variables, ruminant population density, elevation and land cover data. Results: Based on the results of the ExtraTreesClassifier algorithm, 19 variables were identified as important features in prediction which mostly included bioclimatic variables related to temperature. Different combinations of predictive risk factors were evaluated in separate models. ANN and RF algorithms, especially when all predictor variables were included together showed the best performance in predicting the BTV infection occurrence. Conclusions: RF and ANN algorithms outperformed other machine learning methods in predicting the occurrence of BTV infection, especially when all predictive risk factors were included. Moreover, compared to meteorological, ruminant population density, altitude and land cover features, bioclimatic variables especially those related to temperature played a more important role in predicting the occurrence of BTV infection using machine learning algorithms. The results of the present study could be helpful in planning BTV infection surveillance and adopting control and preventive strategies.

Stray cats and dogs are the major risk factors for human toxocariasis and one of the most important public health issues. Accessing and analyzing the prevalence of Toxocara spp. in definitive host may help control and prevention of human toxocariasis. This systematic review and meta-analysis is the first study in Middle east, directed to estimate the prevalence of Toxocara cati, Toxocara canis and Toxocara leonina infection in cats and dogs by gender, age, geographical location, weather condition and other risk factors. Three English databanks (Scopus, PubMed, and Google Scholar) were searched for published articles about Toxocara parasites of cats and dogs in Middle-East from 1980 to 2022. Of 300 peer-reviewed articles, 40 were included in this review and represented cats and dogs from all over the Middle east countries. The collective prevalence (95% CI) of Toxocara cati infection in stray cats was 7.66%, being highest in north of Iran and lowest in Qatar countries. The collective prevalence (95% CI) of Toxocara canis infection in stray dogs was 26.34%, being highest in north of Iran and lowest in Israel countries. The collective prevalence (95% CI) of Toxocara leonina infection in stray cats and dogs was 2.85%, being highest in Egypt and lowest in Qatar countries. Prevalence of Toxocara leonina was higher in low-income tropical countries and also in young (12 months of age) cats and dogs and also higher in humid weather like north of Iran. Control prevention and of this zoonosis should take greater care by health officials particularly in countries where risk factors and prevalence are highest.

No abstract